1.0 Technical Field
The present invention relates generally to computerized video image tracking and more particularly to tracking deformable objects with a computerized active contour model.
2.0 Discussion
Tracking and extraction of deformable objects from a video sequence have significant uses for multimedia systems and image analysis. Its applications include content-based video indexing, image and video manipulation, and model-based video compression. Two principle reasons exist for why object deformation needs to be taken into consideration in those applications. First, most natural objects of interest are non-rigid. Some of them are articulated (e.g., humans, animals, trees, etc.) and others are deformable or elastic. Secondly, rigid objects can also appear to be "deformed" in two-dimensional images, due to the perspective projection and lens distortion effects.
The tracking of deformable objects encounters several problems. A deformable object's structure can change over time and no single motion description can accurately describe the local motion for each subpart of the object. On the other hand, occlusion can cause even more problems during the tracking. The disappearance or re-appearance of a certain part of the object can change the neighborhood characteristic of the contour points which define the contour of an object. This situation usually results in misleading the tracking of the object.
Different models have been suggested to represent deformable objects. Meshes can be used to model object deformation by estimating the motion of each nodal point constrained by a mesh structure. Deformable templates have also been used to find similar objects after some kind of deformation in a video frame (see: A. K. Jain, Y. Zhong, and S. Lakshmanan, Object Matching Using Deformable Templates, IEEE Trans. on Pattern Analysis and Machine Intelligence, March 1996).
Also, the snake approach has been used in extracting objects from still images. A snake is an elastic curve placed on an image object that converges toward image object features that satisfy certain smoothness constraints. To extend it to tracking image objects in image sequences, a reliable motion estimation algorithm is needed and is lacking in past works.
Several researchers have studied snakes combined with motion prediction and estimation for deformable objects contours. Terzopoulos and Szeliski proposed the Kalman snake, which incorporates the dynamic snake into the Kalman filter system for tracking rigid or non-rigid objects (see: D. Terzopoulos and R. Szeliski, Tracking With Kalman Snakes, A. Blake and A. Yuille, editors, Active Vision. MIT Press, Cambridge, Mass., 1992). Ngo et al. proposed the generalized active contour model (called g-snake) for tracking the object boundaries (see: C. W. Ngo, S. Chan and K. F. Lai, Motion Tracking And Analysis Of Deformable Objects By Generalized Active Contour Model, Second Asian Conference on Computer Vision, pages 442-446, 1995). Temporal motion information is used for adaptive global motion prediction of the target object. However, only global motion prediction is used and the lack of local motion estimation can still lead to tracking failure, especially when the image contains complicated edge structures or when the object has significant local deformation.